F62b Checkpoint

Import libraries¶

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import ee
import geemap

import ee import geemap

Create an interactive map¶

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Map = geemap.Map(center=[40, -100], zoom=4)

Map = geemap.Map(center=[40, -100], zoom=4)

Add Earth Engine Python script¶

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# Add Earth Engine dataset
image = ee.Image("USGS/SRTMGL1_003")

#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      F6.2 Scaling Up in Earth Engine
#  Checkpoint:   F62b
#  Authors:      Jillian M. Deines, Stefania Di Tommaso, Nicholas Clinton, Noel Gorelick
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Load county dataset.
# Filter counties in Indiana, Illinois, and Iowa by state FIPS code.
# Select only the unique ID column for simplicity.
countiesAll = ee.FeatureCollection('TIGER/2018/Counties')
states = ['17', '18', '19']
uniqueID = 'GEOID'
featColl = countiesAll.filter(ee.Filter.inList('STATEFP', states)) \
    .select(uniqueID)

print(featColl.size())
print(featColl.limit(1))
Map.addLayer(featColl)

# Specify years of interest.
startYear = 2001
endYear = 2020

# Climate dataset info.
imageCollectionName = 'IDAHO_EPSCOR/GRIDMET'
bandsWanted = ['pr', 'tmmn', 'tmmx']
scale = 4000

# Export info.
exportFolder = 'GEE_scalingUp'
filenameBase = 'Gridmet_counties_IN_IL_IA_' + scale + 'm_'

# Initiate a loop, in which the variable i takes on values of each year.
for i in range(startYear, endYear, 1):         # for each year....

  # Load climate collection for that year.
  startDate = i + '-01-01'

  endYear_adj = i + 1
  endDate = endYear_adj + '-01-01'

  imageCollection = ee.ImageCollection(imageCollectionName) \
      .select(bandsWanted) \
      .filterBounds(featColl) \
      .filterDate(startDate, endDate)

  # Get values at feature collection.

def func_rsk(image):
    return image.reduceRegions({
      'collection': featColl,
      'reducer': ee.Reducer.mean(),
      'tileScale': 1,
      'scale': scale
    }).filter(ee.Filter.NotNull(bandsWanted))  # remove rows without data \
      .map(function(f) {                  
        time_start = image.get('system:time_start')
        dte = ee.Date(time_start).format('YYYYMMdd')
        return f.set('date_ymd', dte)
    })

  sampledFeatures = imageCollection.map(func_rsk
).flatten()











).flatten()

  # Prepare export: specify properties and filename.
  columnsWanted = [uniqueID].concat(['date_ymd'], bandsWanted)
  filename = filenameBase + i

  Export.table.toDrive({
    'collection': sampledFeatures,
    'description': filename,
    'folder': exportFolder,
    'fileFormat': 'CSV',
    'selectors': columnsWanted
  })


#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F6.2 Scaling Up in Earth Engine # Checkpoint: F62b # Authors: Jillian M. Deines, Stefania Di Tommaso, Nicholas Clinton, Noel Gorelick # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Load county dataset. # Filter counties in Indiana, Illinois, and Iowa by state FIPS code. # Select only the unique ID column for simplicity. countiesAll = ee.FeatureCollection('TIGER/2018/Counties') states = ['17', '18', '19'] uniqueID = 'GEOID' featColl = countiesAll.filter(ee.Filter.inList('STATEFP', states)) \ .select(uniqueID) print(featColl.size()) print(featColl.limit(1)) Map.addLayer(featColl) # Specify years of interest. startYear = 2001 endYear = 2020 # Climate dataset info. imageCollectionName = 'IDAHO_EPSCOR/GRIDMET' bandsWanted = ['pr', 'tmmn', 'tmmx'] scale = 4000 # Export info. exportFolder = 'GEE_scalingUp' filenameBase = 'Gridmet_counties_IN_IL_IA_' + scale + 'm_' # Initiate a loop, in which the variable i takes on values of each year. for i in range(startYear, endYear, 1): # for each year.... # Load climate collection for that year. startDate = i + '-01-01' endYear_adj = i + 1 endDate = endYear_adj + '-01-01' imageCollection = ee.ImageCollection(imageCollectionName) \ .select(bandsWanted) \ .filterBounds(featColl) \ .filterDate(startDate, endDate) # Get values at feature collection. def func_rsk(image): return image.reduceRegions({ 'collection': featColl, 'reducer': ee.Reducer.mean(), 'tileScale': 1, 'scale': scale }).filter(ee.Filter.NotNull(bandsWanted)) # remove rows without data \ .map(function(f) { time_start = image.get('system:time_start') dte = ee.Date(time_start).format('YYYYMMdd') return f.set('date_ymd', dte) }) sampledFeatures = imageCollection.map(func_rsk ).flatten() ).flatten() # Prepare export: specify properties and filename. columnsWanted = [uniqueID].concat(['date_ymd'], bandsWanted) filename = filenameBase + i Export.table.toDrive({ 'collection': sampledFeatures, 'description': filename, 'folder': exportFolder, 'fileFormat': 'CSV', 'selectors': columnsWanted }) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map